Emoroberta
E
Emoroberta
Developed by arpanghoshal
A fine-grained sentiment classification model based on the RoBERTa architecture, trained on the GoEmotions dataset, capable of recognizing 28 emotion categories.
Downloads 21.47k
Release Time : 3/2/2022
Model Overview
This model is specifically designed for text sentiment analysis, capable of identifying 28 different emotion categories including happiness, anger, sadness, etc. It is suitable for emotion recognition in scenarios such as social media comments and user feedback.
Model Features
Fine-grained Emotion Recognition
Supports fine-grained classification of 28 emotion categories, far exceeding traditional three-category models (positive/negative/neutral).
Improved Based on RoBERTa
Utilizes an optimized RoBERTa architecture, enhancing performance through hyperparameter tuning and expanded training data.
Optimized for Reddit Comments
Trained on the GoEmotions dataset (58,000 Reddit comments), making it particularly suitable for social media text analysis.
Model Capabilities
Text Sentiment Classification
Fine-grained Emotion Recognition
Social Media Comment Analysis
Use Cases
Social Media Analysis
User Comment Sentiment Analysis
Analyze the emotional tendencies of user comments on platforms like Reddit
Can identify 28 specific emotional states
Customer Feedback Analysis
Product Feedback Emotion Classification
Perform fine-grained sentiment classification on customer feedback
Accurately identifies details of customer satisfaction (e.g., gratitude, disappointment, etc.)
Featured Recommended AI Models